Being able to determine the physical location of a mobile device is important for ensuring consistent and accurate mobile communications. Over time, various techniques have been employed to identify, locate and “track and trace” devices in proximity to a communications tower, cellular Point of Presence (POP), etc. These techniques have often been refined in an attempt to improve the precision of detection of the device and also to assist in determining various linkage schemes to enhance cellular tower handoffs occurring when a device traverses across an area have a multitude of POPs.
Recently, the introduction of location awareness needs in and for devices has risen in the market primarily as a result of the demands from software applications (apps) which operate in conjunction with the mobile device (e.g., smartphone) require the location of the device to be known by the app for satisfactory operations. In order to operate effectively, the app must be made aware of the location of the device and be aware of how to accept location information having the same. Typically, an app designer coordinates the delivery of device location data to the app and many apps are designed to accept location information from a variety of sources. These designs typically rely on using Global Positioning System (GPS) Lookup and/or network-based positioning methods such as Cell Tower Lookup information, for example.
App designers also routinely engage several retrieval techniques to obtain device location information that may be resident or available as related to a device, often depending on its operating system (OS). For instance, for a Windows® mobile device, both Windows Mobile 5.0 and Windows Mobile 6 devices are known to contain an abstraction layer known as the GPS Intermediate Driver (GPSID) between the device driver that controls the GPS receiver and applications desirous of location or GPS information. In these situations, device location information is retrieved synchronously or asynchronously, including continued update availability for the latter situation, in direct relation to the location data obtained by the GPS radio. Similarly, mobile applications, such as Machine-to-Machine (M2M) apps, often require associated devices to report their respective location information to a central server or station periodically. In general, it is widely understood that app operations utilize and consume location information of devices at an ever-increasing rate.
However, in each of the above referenced scenarios, time is required to determine location information for each specific device and additional time is required where searches for devices in a geographical area (local, national, international) may be necessitated. In situations where there are tens of thousands of devices, for instance, the time required may be within the objectives of a communications system based on search interests; however, in situations where there may exist devices in quantities of factors greater than such, or where the search parameters are more arbitrary than specific, present techniques may prove inadequate for system objectives and performance needs.
For example, in a situation where there are millions of devices deployed across international locations ranging from metropolitan cities to remote continental locations, the ability to efficiently determine which devices are in a random geographical area at any specific time is inadequate using present methods as, in part, location information is both spatial and temporal.
To overcome this, attempts using present methods may employ a naïve linear scan (NLS) to examine location reports from every device as against a geographical search boundary. However, given the volume of devices, it is inconceivable that a NLS approach may operate efficiently, where a matching is required to be computed between a query and every location reporting the database, for instance, as the required search time likely linearly increases in relation to the number of devices of the inquiry. Further, where search boundaries of interest may be further complicated by non-predetermined, or randomized, geographical search interests, additional time and resources are further required. As a result, present methods and the NLS approach are generally impractical for most present and developing situations and more particularly, these approaches lack efficiencies in operation for situations of increasing devices having location information that is changing; similarly, these approaches are also inadequate as they often fail to take into consideration issues associated with time (i.e., temporally) in relation to effects of changing locations and associated information.
Accordingly, what is desired is to provide a system and method to locate mobile devices in arbitrary geographical boundaries in an efficient and scalable search methodology, preferably on a large-scale basis.
As used herein the terms device, third party system, smart phone, terminal, remote device, wireless asset, etc. are intended to be inclusive, interchangeable, and/or synonymous with one another and other similar communication-based equipment for purposes of the present invention though one will recognize that functionally each may have unique characteristics, functions and/or operations which may be specific to its individual capabilities and/or deployment.